1. Field
The subject matter disclosed herein relates to classifying user activity in a mobile device.
2. Information
Many mobile communication devices, such as smartphones, include an inertial sensor, such as an accelerometer, that may be used to detect motion of the device. These movements may be useful in determining the device's orientation so that a display may be properly oriented, for example in a portrait or a landscape mode, when displaying information to a user. In another example, a gaming application performed by way of a smartphone may rely on movements detected by one or more accelerometers so that a feature of the game may be controlled. In other examples, a gesturing movement detected by an accelerometer may allow a user to scroll a map, navigate a menu, or control other aspects of the device's operation.
Though useful in assisting with simple user interface tasks, it has not been possible to make use of the output signals or “traces” of an accelerometer to provide more sophisticated and meaningful assistance to mobile device users. For example, if it can be detected that a user is engaged in a strenuous activity, it may be useful to direct incoming telephone calls immediately to voicemail so as not to distract the user. In another example, if it can be detected that a mobile device is in a user's purse or pocket, it may be advantageous to disable a display so as not to waste battery resources.
When attempting to infer user activity, such as walking, running, cycling, and so forth, various techniques may be used to acquire a signal from an inertial sensor, extract features from the acquired signal, and infer an activity class. However, when estimating a user's activity class, a trade-off may be made between performing an accurate estimation of a user's activity and performing the estimation in a timely manner. In general, accurate estimations may be available, but only after a processing delay.